CN106534002A - Power line channel estimation method based on compressed sensing - Google Patents

Power line channel estimation method based on compressed sensing Download PDF

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CN106534002A
CN106534002A CN201610841846.7A CN201610841846A CN106534002A CN 106534002 A CN106534002 A CN 106534002A CN 201610841846 A CN201610841846 A CN 201610841846A CN 106534002 A CN106534002 A CN 106534002A
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power line
signal
line channel
channel
compressed
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CN106534002B (en
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张翼英
杨巨成
梁琨
赵青
刘颖
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Tianjin University of Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/022—Channel estimation of frequency response
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/025—Channel estimation channel estimation algorithms using least-mean-square [LMS] method
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0264—Arrangements for coupling to transmission lines
    • H04L25/0266—Arrangements for providing Galvanic isolation, e.g. by means of magnetic or capacitive coupling
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2689—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation
    • H04L27/2695—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation with channel estimation, e.g. determination of delay spread, derivative or peak tracking

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)

Abstract

The invention relates to a power line channel estimation method based on compressed sensing. The steps are as follows: 1) signal sparse representation; 2) data compression; and 3) compressed data reconstruction, wherein a receiving end estimates an impact response h(t) of a power line channel through the adoption of a sending reconstruction algorithm by use of an extracted power line channel feature y, thereby performing the accurate channel estimation on the power line channel. Aiming at the sparseness of the power line channel, the invention provides a power line channel estimation method based on the compressed sensing; the method realizes the effective evaluation on the power line channel, and has the features of being small in channel occupation and less storage content; a simulation result indicates that the power line channel estimation method based on the compressed sensing uses less pilot signal and less storage resource, but the algorithm performance is superior to the minimum least square channel estimation algorithm; therefore, the purposed algorithm has better application prospect.

Description

A kind of power line channel method of estimation based on compressed sensing
Technical field
The invention belongs to technical field of electric power, especially a kind of power line channel method of estimation based on compressed sensing.
Background technology
Due to power line network be for transmitting electric energy design, therefore, power line channel characteristic and other common communications Characteristic of channel very different, the noise on power line has not been single white Gaussian noise in other common communication environments, electricity Line of force noise is extremely complex.With electrical equipment Stochastic accessing and cut out, with very strong time-varying characteristics, and channel status Information is most important for related data detection, channel quantitative and AF panel etc..Therefore, power line channel transmission characteristic with And power line channel method of estimation needs further analysis and studies.
Traditional channel estimation methods generally comprise non-blind Channel Estimation, blind Channel Estimation and semi-blind channel estimation.It is blind Channel estimation methods, receiving terminal only obtain channel condition information according to the unknown data statistics for receiving.Blind Channel is estimated Although meter is possible in theory, due to needing substantial amounts of data, it is high to process complexity, for fast-changing channel, often not System requirements can be met.Based on the channel estimation methods of training sequence, transmitting terminal sends known instruction in specific time domain, frequency domain Practice sequence, and receiving terminal estimates channel condition information, typical channel estimation side according to the training sequence after wireless channel Method has least-squares algorithm, least-mean-square error algorithm.But the channel estimation methods based on training sequence need extra instruction Practice sequence and reduce spectrum efficiency, carry out in the abundant multipath channel of scattering, these methods need more training sequence Row, and training sequence does not carry useful information, so as to reduce the availability of frequency spectrum.To sum up, existing these methods need very high Analog-to-digital conversion speed, receiving terminal needs to send in order to accurately estimate the characteristic of channel, then very long pilot signal, and gathers big The sample data of amount, considerably increases the hardware complexity and hardware cost of receiving terminal.
Research shows that power line channel transmission characteristic is linear time-variant channel, can use the pilot signal based on OFDM Estimate power line channel transmission characteristic.OFDM is that channel is divided into some sub-channels, by the serial data stream of high speed input Some parallel low rate data streams are converted into, modulation is transmitted on every sub-channels, and these subchannels are orthogonal.Connecing Receiving end is demodulated using correlation technique, then turns to serial data stream.OFDM is a kind of Multicarrier Transmission Technology, an OFDM Comprising multiple subcarriers through modulation in symbol.The frequency efficiency of OFDM technology is high, in frequency spectrum as power line communication In the case of limited, OFDM technology is effective against the multipath effect and frequency selective fading of power line channel presence.
Due to being typically all using the emanant distribution system combined with trunk formula in domestic residential area.Therefore, electricity There is a large amount of branched structures and impedance mismatch node in powerline networks.The node of these impedance mismatchs is caused on power line The signal being transmitted can not directly reach receiving node from sending node, but reflection can occur on the different path of each bar And standing wave, so, the information signal for finally giving is mainly reflection and the later superposed signal of standing wave on different paths so that electricity Line of force channel is presented multipath effect, and transmission feature can show certain frequency selective fading.In order to be best understood from Impact of the power line channel communication environment to PLC device performance, it is to be understood that the characteristic of channel of power line, it is therefore necessary to PLC Power line channel carries out accurate channel estimation, obtains the characteristic parameter of channel impulse response.OFDM technology to frequency shift (FS) and Phase noise is very sensitive.Peak value and average power are relatively large, and this can affect the power efficiency of radio frequency amplifier, and its channel is estimated Meter mechanism have ignored the requirement to receiving terminal ADC devices, cause power line channel to estimate that assessment is difficult, while in pilot signal machine The aspects such as storage resource need a large amount of supports.
By retrieval, the patent publication us related to present patent application are not yet found.
The content of the invention
It is an object of the invention in place of overcoming the deficiencies in the prior art, there is provided a kind of power line based on compressed sensing is believed Channel estimation method, the method are realized to the effective assessment of power line channel, and have the spies such as preferable channel occupancy is little, amount of storage is few Property.
The technical solution adopted for the present invention to solve the technical problems is：
A kind of power line channel method of estimation based on compressed sensing, step are as follows：
1) sparse signal representation
Signal s is openness in the time domain lack in the case of, another time domain is transformed to it by projection and obtains sparse letter Number x, and the x that checks the number carries out being effectively compressed process；Shown in the thinning processes such as formula (1) of whole transform domain projection；
Wherein, s primary signals,It is projection matrix, x is s in projection matrixOn projection, so as to complete the sparse of s Change；
2) data compression
If x (n) is the N-dimensional data signal that ADC samplings are obtained, if the signal degree of rarefication is K, i.e., only K element is not Zero, dimensionality reduction is carried out to x (n) using compressed sensing technology and is compressed to M dimensions, obtain signal y, as shown in formula (2)；
Wherein, y is the signal after being compressed, and φ is suitable observing matrix, and x is the signal represented by rarefaction, and s is former Begin signal to be compressed；
3) compressed data reconstruct
Consider energy consumption and efficiency, data compression is realized using hardware in front end, reduce storage analysis；Therefore, study hardware The compressed data recovery and rebuilding algorithm of rear end；Based on above compression algorithm, formula (3) can be passed through by the recovery and rebuilding of compressed signal Solve minimum 0 norm to realize；
minx||x||0S.t.y=φ x (3)
In formula (3), x is the sparse signal with reconstruct, and y is the signal recovered after the observation to x, during 0 norm refers to x The number of nonzero element；
Traditional LS channel estimation by transmitting and the isometric pilot blocks of channel impulse response, so as to by transmission signal Cyclic convolution is converted to the linear convolution of channel impulse response, vector is received and is written as formula (4)；
Y=p*h+n (4)
In formula, * represents cyclic convolution；P is the pilot signal for sending, and above formula further write as formula (5)；
Y=Ch+n (5)
After constructing suitable pilot signal, in originator transmitting test pilot signal C, it is coupled on power line by coupler It is transmitted, pilot signal passes through the impact of power line channel transmission characteristic and power line noise, passes through coupler in receiving terminal Receive through decay and by the pilot signal of noise jamming；Receiving terminal receives signal and is represented by formula (6)；
Receiving terminal estimates the impact of power line channel using power line channel feature y extracted with sensing reconstructing algorithm Response h (t), you can channel estimation exactly is carried out to power line channel.
And, the sensing reconstructing algorithm is orthogonal matching pursuit algorithm.
And, the recovery and rebuilding step of the orthogonal matching pursuit algorithm is as follows：
1) initialize：Residual values r0=y, indexed set Λ0=Φ, iterations i=1, Γ0=Φ；
2) determine index value：λi+1=arg max |<ri,τj>|, τjJth for matrix Φ is arranged；It is determined that the position of correspondence atom Put, i.e. the position of nonzero element：{Λi+1=Λi∪λi+1, { Γi+1=Γi∪τλi+1}；
3) new estimate is obtained with LS algorithms：
Wherein ,+represent pseudoinverse；
4) new residual values are calculated：
5) it is optimized iterative process：Construction circulates i=i+1, then repetition index process, until completing the iteration for specifying During number of times m, terminate iteration；
6) complete signal reconstruction：It is calculated estimateMeet below equation：
And
Orthogonal matching pursuit algorithm will once find the position corresponding to a nonzero element in x per iteration, and count The value of the element is calculated, the estimate of whole x is can be obtained by after m iterationThe impact of power line channel is estimated Response.
The advantage of present invention acquirement and good effect are：
1st, the inventive method possesses openness for power line communication channel, it is proposed that a kind of electric power based on compressed sensing Line channel estimation methods, the method achieve assessment effective to power line channel, and there is preferable channel to take little, amount of storage The characteristic such as few；Simulation result shows, based on the power line channel method of estimation of compressed sensing used less pilot signal and Less storage resource, but algorithm performance is but better than LS channel estimation algorithm, therefore the algorithm for proposing possesses more Good application prospect.
2nd, this method sends suitable pilot signal with compressed sensing technology in transmitting terminal, amplifies coupling through termination power Close on power line, analyze the sparse characteristic that power line channel itself has, through power line channel time delay and decay, connecing Receiving end is extracted to power line channel validity feature, is isolated and is received by termination power forceful electric power, then is turned by ADC moduluses Change, power line channel estimation is completed through Digital Signal Processing.Meanwhile, use less pilot signal and less storage money Source.
3rd, this method has used OMP algorithms (orthogonal matching pursuit algorithm), selects and observation signal is maximum from atom Matched atoms carry out Schmidt orthogonalization process, make signal projection with orthogonality, make OMP algorithms atom in an iterative process Selection does not repeat, it is ensured that iteration optimality, so as to reduce iterations.
4th, power line channel of this method based on CS is estimated compared with traditional LS channel estimation, in signal to noise ratio not Height, in the case that channel circumstance is relatively severe, there is the channel estimation carried out with compressed sensing technology the more preferable characteristic of channel to estimate Meter effect, its evaluated error is than more than little ten times of traditional LS channel estimation algorithm.
Description of the drawings
Fig. 1 is PLC compressed sensing procedure charts in the present invention；
Fig. 2 is reference channel amplitude-frequency response figure in the present invention；
Fig. 3 is reference channel shock response figure in the present invention.
Specific embodiment
With reference to embodiment, the present invention is further described；Following embodiments are illustrative, be not it is determinate, Protection scope of the present invention can not be limited with following embodiments.
Method used in the present invention, if no special instructions, is the conventional method of this area.
This method sends suitable pilot signal with compressed sensing technology in transmitting terminal, amplifies through termination power and couples To on power line, the sparse characteristic that power line channel itself has is analyzed, through power line channel time delay and decay, received End is extracted to power line channel validity feature, is isolated and is received by termination power forceful electric power, then by ADC analog-to-digital conversions, Power line channel estimation is completed through Digital Signal Processing.Meanwhile, use less pilot signal and less storage resource.
A kind of power line channel method of estimation based on compressed sensing, step are as follows：
In order to be best understood from impact of the power line channel communication environment to PLC device performance, it is to be understood that electric power The characteristic of channel of line, it is therefore necessary to carry out accurate channel estimation to PLC power line channels, obtains the feature of channel impulse response Parameter, its process are as shown in Figure 1.
1) sparse signal representation
Signal s is openness in the time domain lack in the case of, another time domain can be transformed to it by projection and obtain dilute Thin signal x, and the x that checks the number carries out being effectively compressed process；Shown in the thinning processes such as formula (1) of whole transform domain projection；
Wherein, s primary signals,It is projection matrix, x is s in projection matrixOn projection, so as to complete the sparse of s Change；
2) data compression
If x (n) is the N-dimensional data signal that ADC samplings are obtained, if the signal degree of rarefication is K, i.e., only K element is not Zero, dimensionality reduction is carried out to x (n) using compressed sensing technology and is compressed to M dimensions, obtain signal y, as shown in formula (2)；
Wherein, y is the signal after being compressed, and φ is suitable observing matrix, and x is the signal represented by rarefaction, and s is former Begin signal to be compressed；
3) compressed data reconstruct
Consider energy consumption and efficiency, data compression is realized using hardware in front end, reduce storage analysis；Therefore, it is main to study The compressed data recovery and rebuilding algorithm of hardware rear end；Based on above compression algorithm, can be by public affairs by the recovery and rebuilding of compressed signal Formula (3) solves minimum 0 norm to realize；
minx||x||0S.t.y=φ x (3)
In formula (3), x is the sparse signal with reconstruct, and y is the signal recovered after the observation to x, during 0 norm refers to x The number of nonzero element；
Traditional LS channel estimation by transmitting and the isometric pilot blocks of channel impulse response, so as to by transmission signal Cyclic convolution is converted to the linear convolution of channel impulse response, vector is received and can be written as formula (4)；
Y=p*h+n (4)
In formula, * represents cyclic convolution；P is the pilot signal for sending, and above formula can further be write as formula (5)；
Y=Ch+n (5)
After constructing suitable pilot signal, in originator transmitting test pilot signal C, it is coupled on power line by coupler It is transmitted, pilot signal passes through the impact of power line channel transmission characteristic and power line noise, passes through coupler in receiving terminal Receive through decay and by the pilot signal of noise jamming；Receiving terminal receives signal and can be represented by formula (6)；
Receiving terminal can using extract power line channel feature y, with sensing reconstructing algorithm (for example, OMP algorithms, it is orthogonal Matching pursuit algorithm) estimate power line channel shock response h (t).
OMP algorithms are the innovatory algorithms of MP algorithms, and OMP algorithms are selected from atom and observation signal maximum matched atoms Schmidt orthogonalization process being carried out, signal projection being made with orthogonality, atom selects not weigh in an iterative process to make OMP algorithms It is multiple, it is ensured that iteration optimality, so as to reduce iterations.Concrete OMP recovery and rebuilding steps are as follows：
1) initialize：Residual values r0=y, indexed set Λ0=Φ, iterations i=1, Γ0=Φ；
2) determine index value：λi+1=arg max |<ri,τj>|, τjJth for matrix Φ is arranged；It is determined that the position of correspondence atom Put, i.e. the position of nonzero element：{Λi+1=Λi∪λi+1, { Γi+1=Γi∪τλi+1}；
3) new estimate is obtained with LS algorithms：
Wherein ,+represent pseudoinverse；
4) new residual values are calculated：
5) it is optimized iterative process：Construction circulates i=i+1, then repetition index process, until completing the iteration for specifying During number of times m, terminate iteration；
6) complete signal reconstruction：It is calculated estimateMeet below equation：
And
OMP algorithms will once find the position corresponding to a nonzero element in x per iteration, and calculate the element Value, can be obtained by the estimate of whole x after m iterationThe shock response of power line channel is estimated.
The power line channel shock response for using is that, based on Matlab emulation platform the data obtaineds, PLC channels time-frequency domain rings Should be that, in 15 paths, longest path is the channel model that generates under the conditions of 1000m.And the power line channel based on CS is estimated Comparative analysis has been carried out under the same conditions with the LS channel estimation based on pilot frequency sequence.
With reference to reference channel parameter value, power line reference channel time and frequency domain characteristics, the electric power that Matlab emulation is generated is emulated The simulation bandwidth B of line transmission featurew=30MHz, sample frequency fs=60MHz, due to the maximum of power line channel in practice Time delay not over 10 μ s, so sampling time t=10 μ s.The power line channel transmission feature frequency domain amplitude-frequency response of generation and Time domain impulse is responded as shown in Figure 2 and Figure 3.
Based on the power line channel shock response that emulation is obtained, suitable pilot signal matrix, fixed reception signal is constructed Dimension M be 200 for 150, N, power line channel is disturbed by ambient noise so that signal to noise ratio is constantly increased by 5dB to 30dB It is big to change.Based on CS power line channel estimate compared with traditional LS channel estimation, channel ring not high in signal to noise ratio In the case that border is relatively severe, the channel estimation carried out with compressed sensing technology has more preferable channel characteristic estimation effect, its Evaluated error is than more than little ten times of traditional LS channel estimation algorithm.
Power line channel based on CS is estimated compared with traditional LS channel estimation, constant in power line signal to noise ratio In the case of, with the continuous increase for receiving signal dimension M, the power line channel of two kinds of methods of estimation estimates that performance can be Improve, but as power line channel itself has sparse characteristic, and exactly make use of based on the power line channel method of estimation of CS This itself sparse characteristic of power line channel enables the method to the estimation power line channel characteristic of more efficiently and accurately, reaches To preferable channel recovery and rebuilding effect.

Claims (3)

1. a kind of power line channel method of estimation based on compressed sensing, it is characterised in that：Step is as follows：
1) sparse signal representation
Signal s is openness in the time domain lack in the case of, another time domain is transformed to it by projection and obtains sparse signal x, And the x that checks the number carries out being effectively compressed process；Shown in the thinning processes such as formula (1) of whole transform domain projection；
Wherein, s primary signals,It is projection matrix, x is s in projection matrixOn projection, so as to complete the rarefaction of s；
2) data compression
If x (n) is the N-dimensional data signal that ADC samplings are obtained, if the signal degree of rarefication is K, i.e., only K element is not zero, adopts Dimensionality reduction is carried out to x (n) with compressed sensing technology and is compressed to M dimensions, obtain signal y, as shown in formula (2)；
Wherein, y is the signal after being compressed, and φ is suitable observing matrix, and x is the signal represented by rarefaction, and s is original treating Compressed signal；
3) compressed data reconstruct
Consider energy consumption and efficiency, data compression is realized using hardware in front end, reduce storage analysis；Therefore, study hardware rear end Compressed data recovery and rebuilding algorithm；Based on above compression algorithm, can be solved by formula (3) by the recovery and rebuilding of compressed signal Minimum 0 norm is realizing；
minx||x||0S.t.y=φ x (3)
In formula (3), x is the sparse signal with reconstruct, and y is the signal recovered after the observation to x, and 0 norm refers to non-zero in x The number of element；
Traditional LS channel estimation by transmitting and the isometric pilot blocks of channel impulse response, so as to by transmission signal and letter The linear convolution of road shock response is converted to cyclic convolution, receives vector and is written as formula (4)；
Y=p*h+n (4)
In formula, * represents cyclic convolution；P is the pilot signal for sending, and above formula further write as formula (5)；
Y=Ch+n (5)
After constructing suitable pilot signal, in originator transmitting test pilot signal C, being coupled on power line by coupler is carried out Transmission, pilot signal pass through the impact of power line channel transmission characteristic and power line noise, are received by coupler in receiving terminal Decay and by the pilot signal of noise jamming to passing through；Receiving terminal receives signal and is represented by formula (6)；
Receiving terminal estimates the shock response h of power line channel using power line channel feature y extracted with sensing reconstructing algorithm (t), you can channel estimation exactly is carried out to power line channel.
2. the power line channel method of estimation based on compressed sensing according to claim 1, it is characterised in that：The perception Restructing algorithm is orthogonal matching pursuit algorithm.
3. the power line channel method of estimation based on compressed sensing according to claim 2, it is characterised in that：It is described orthogonal The recovery and rebuilding step of matching pursuit algorithm is as follows：
1) initialize：Residual values r0=y, indexed set Λ0=Φ, iterations i=1, Γ0=Φ；
2) determine index value：λi+1=arg max |<ri,τj>|, τjJth for matrix Φ is arranged；It is determined that the position of correspondence atom, i.e., The position of nonzero element：{Λi+1=Λi∪λi+1, { Γi+1=Γi∪τλi+1}；
3) new estimate is obtained with LS algorithms：
x ^ i + 1 = arg max | | y - &Gamma; i + 1 x ^ | | = &Gamma; i + 1 + x
Wherein ,+represent pseudoinverse；
4) new residual values are calculated：
5) it is optimized iterative process：Construction circulates i=i+1, then repetition index process, until completing the iterations for specifying During m, terminate iteration；
6) complete signal reconstruction：It is calculated estimateMeet below equation：
And
Orthogonal matching pursuit algorithm will once find the position corresponding to a nonzero element in x per iteration, and calculate The value of the element, can be obtained by the estimate of whole x after m iterationThe impact for estimating power line channel rings Should.
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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107395536A (en) * 2017-07-20 2017-11-24 西北工业大学 The method estimated the underwater acoustic channel impulse response function under more way environment
CN107396131A (en) * 2017-08-22 2017-11-24 四川长虹电器股份有限公司 A kind of mobile terminal local datastore method based on compressed sensing
CN108924148A (en) * 2018-07-18 2018-11-30 中南大学 A kind of source signal collaborative compression perception data restoration methods
CN110686846A (en) * 2019-10-11 2020-01-14 河海大学常州校区 Impact monitoring system adopting digital random demodulation and splitting recovery algorithm
CN111614584A (en) * 2020-05-20 2020-09-01 西安电子科技大学 Transform domain adaptive filtering channel estimation method based on neural network
CN113271269A (en) * 2021-04-22 2021-08-17 重庆邮电大学 Sparsity self-adaptive channel estimation method based on compressed sensing
CN114268346A (en) * 2021-11-26 2022-04-01 国网河南省电力公司信息通信公司 Power line carrier communication compressed sensing channel estimation method under non-Gaussian noise
CN115560795A (en) * 2022-12-02 2023-01-03 小米汽车科技有限公司 Air duct blockage detection method and device suitable for charging equipment

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101494627A (en) * 2009-03-11 2009-07-29 北京邮电大学 Channel estimation method for reducing pilot number by using compression perception in wideband mobile communication
US20100322327A1 (en) * 2009-06-23 2010-12-23 Giuseppe Caire Method of estimating and removing noise in OFDM systems
US20120082252A1 (en) * 2010-09-30 2012-04-05 Ramesh Annavajjala Method for Estimating Time-Varying and Frequency-Selective Channels
CN103209067A (en) * 2013-04-15 2013-07-17 深圳市力合微电子有限公司 Power line OFDM (Orthogonal Frequency Division Multiplexing) pilot symbol generation method and device
CN103905350A (en) * 2014-04-08 2014-07-02 电子科技大学 S-IFFT-based channel estimation method for OFDM system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101494627A (en) * 2009-03-11 2009-07-29 北京邮电大学 Channel estimation method for reducing pilot number by using compression perception in wideband mobile communication
US20100322327A1 (en) * 2009-06-23 2010-12-23 Giuseppe Caire Method of estimating and removing noise in OFDM systems
US20120082252A1 (en) * 2010-09-30 2012-04-05 Ramesh Annavajjala Method for Estimating Time-Varying and Frequency-Selective Channels
CN103209067A (en) * 2013-04-15 2013-07-17 深圳市力合微电子有限公司 Power line OFDM (Orthogonal Frequency Division Multiplexing) pilot symbol generation method and device
CN103905350A (en) * 2014-04-08 2014-07-02 电子科技大学 S-IFFT-based channel estimation method for OFDM system

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107395536A (en) * 2017-07-20 2017-11-24 西北工业大学 The method estimated the underwater acoustic channel impulse response function under more way environment
CN107395536B (en) * 2017-07-20 2020-09-08 西北工业大学 Method for estimating underwater sound channel impulse response function in multi-path environment
CN107396131A (en) * 2017-08-22 2017-11-24 四川长虹电器股份有限公司 A kind of mobile terminal local datastore method based on compressed sensing
CN108924148A (en) * 2018-07-18 2018-11-30 中南大学 A kind of source signal collaborative compression perception data restoration methods
CN110686846A (en) * 2019-10-11 2020-01-14 河海大学常州校区 Impact monitoring system adopting digital random demodulation and splitting recovery algorithm
CN111614584A (en) * 2020-05-20 2020-09-01 西安电子科技大学 Transform domain adaptive filtering channel estimation method based on neural network
CN111614584B (en) * 2020-05-20 2021-11-23 西安电子科技大学 Transform domain adaptive filtering channel estimation method based on neural network
CN113271269A (en) * 2021-04-22 2021-08-17 重庆邮电大学 Sparsity self-adaptive channel estimation method based on compressed sensing
CN114268346A (en) * 2021-11-26 2022-04-01 国网河南省电力公司信息通信公司 Power line carrier communication compressed sensing channel estimation method under non-Gaussian noise
CN115560795A (en) * 2022-12-02 2023-01-03 小米汽车科技有限公司 Air duct blockage detection method and device suitable for charging equipment
CN115560795B (en) * 2022-12-02 2023-07-04 小米汽车科技有限公司 Air duct blocking detection method and device suitable for charging equipment

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